ESM 244 Assignment 2 Task 1

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CA DFW Oil Spill Incidents in California (2008)

Code

library(tidyverse)
library(here)
library(broom)

# Spatial data packages
library(sf)
library(tmap)

Data cleaning and wrangling

Pseudocode

• First, read in both datasets and ensure they are read in using the correct file type
• Then fix the projections to be the same
• join the datasets into one
• plot and examine attributes!

Code

#read in the CA Counties shapefile
ca_counties_raw_sf <- read_sf(here("a2_task1_lecuona_sophia", "data", "ca_counties_copy", "CA_Counties_TIGER2016.shp"))

# clean names and go from m^2 to km^2 for land area
ca_counties_sf <- ca_counties_raw_sf %>% 
  janitor::clean_names() %>%
  mutate(land_km2 = aland / 1e6) %>%
  select(county = name, land_km2)

# ca_counties_sf %>% st_crs()
# ca_counties_sf %>% terra::crs() 
# ^ done to see it is currently WGS 84 / Pseudo-Mercator

Code

#read in the Oil Spill Incident Tracking shapefile
oil_raw_sf <- read_sf(here("a2_task1_lecuona_sophia", "data", "ca_oil", "Oil_Spill_Incident_Tracking_[ds394].geojson"))

# oil_sf <- oil_raw_sf %>%
#   select(-LATITUDE, -LONGITUDE)

write_sf(oil_raw_sf, here("a2_task1_lecuona_sophia", "data", "ca_oil", "oil_tracking.gpkg"))

oil_sf <- read_sf(here("a2_task1_lecuona_sophia", "data", "ca_oil", "oil_tracking.gpkg")) %>%
  janitor::clean_names() %>%
  select(-latitude, -longitude)

# Check the CRS:
# oil_sf %>% st_crs()
# oil_sf%>% terra::crs() 
# ^ currently WGS 84 

Code

#exploratory plot
plot(oil_sf %>% select(objectid))

Code

# transform the projection to match:
oil_projected_sf <- st_transform(oil_sf, st_crs(ca_counties_sf))
# oil_projected_sf %>% st_crs() 
# check it! Yay it worked.  Both are WGS 84 / Pseudo-Mercator

Code

ggplot() +
  geom_sf(data = ca_counties_sf) +
  geom_sf(data = oil_projected_sf, size = 1, color = "red")

Code

# spatial join!
ca_and_oil_sf <- ca_counties_sf %>% 
  st_join(oil_projected_sf)

# by counts of incidents
ca_oil_counts_sf <- ca_and_oil_sf %>% 
  group_by(county) %>%
  summarize(n_records = sum(!is.na(objectid)))

# # change 'NA' row to be more intuitive:
# ca_oil_counts_sf[58, 1] <- "Pacific Ocean"

head(ca_oil_counts_sf)

Simple feature collection with 6 features and 2 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -13668380 ymin: 4502606 xmax: -13307390 ymax: 4888059
Projected CRS: WGS 84 / Pseudo-Mercator
# A tibble: 6 × 3
  county    n_records                                                   geometry
  <chr>         <int>                                         <MULTIPOLYGON [m]>
1 Alameda         188 (((-13612247 4538150, -13612347 4538291, -13612423 453844…
2 Alpine            1 (((-13366504 4678946, -13366493 4678954, -13366468 467897…
3 Amador            8 (((-13472698 4647652, -13472698 4647677, -13472698 464775…
4 Butte            11 (((-13565005 4798394, -13564992 4798529, -13565000 479854…
5 Calaveras         7 (((-13428575 4627725, -13428535 4627889, -13428535 462809…
6 Colusa            4 (((-13589905 4781178, -13589881 4781178, -13589804 478117…

Exploratory interactive map

Code

# Set the viewing mode to "interactive":
tmap_mode(mode = "view")

tm_shape(ca_counties_sf) +
  tm_fill("land_km2", palette = c("#B0E0E6", "#DAA520","#A0522D")) +
  tm_shape(oil_projected_sf) +
  tm_dots(col= "darkblue") +
  tm_layout(title = "Oil Spill Incident Tracking",
            title.size = 1.5,
            frame = FALSE)

Code

# legend.title = "CA County Sizes (km^2)",

See:

• tmap vignettes
• Chapter 8 in Robin Lovelace’s “Geocomputation in R”

Finalized static choropleth map

Code

#plot choropleth
ggplot(data = ca_oil_counts_sf) +
  geom_sf(aes(fill = n_records), color = "white", size = 0.1) +
  scale_fill_gradientn(colors = c("#B0E0E6", "#DAA520","#A0522D")) +
  theme_minimal() +
  labs(fill = "Number of Oil Incidence Reports")

Point pattern analysis of oil spill occurances